Overview

Dataset statistics

Number of variables20
Number of observations28470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory160.0 B

Variable types

Numeric17
Categorical3

Alerts

data has a high cardinality: 146 distinct values High cardinality
posicao has a high cardinality: 195 distinct values High cardinality
df_index is highly correlated with nestaHigh correlation
pr is highly correlated with umidadeHigh correlation
umidade is highly correlated with prHigh correlation
EMI is highly correlated with nino3High correlation
nino3 is highly correlated with EMIHigh correlation
atn is highly correlated with atlgradHigh correlation
ats is highly correlated with atl3 and 1 other fieldsHigh correlation
atlgrad is highly correlated with atn and 2 other fieldsHigh correlation
atl3 is highly correlated with ats and 2 other fieldsHigh correlation
seta is highly correlated with ats and 2 other fieldsHigh correlation
nesta is highly correlated with df_indexHigh correlation
df_index is highly correlated with nestaHigh correlation
pr is highly correlated with umidadeHigh correlation
umidade is highly correlated with prHigh correlation
EMI is highly correlated with nino3High correlation
nino3 is highly correlated with EMIHigh correlation
atn is highly correlated with atlgradHigh correlation
ats is highly correlated with atlgrad and 2 other fieldsHigh correlation
atlgrad is highly correlated with atn and 3 other fieldsHigh correlation
atl3 is highly correlated with ats and 2 other fieldsHigh correlation
seta is highly correlated with ats and 2 other fieldsHigh correlation
nesta is highly correlated with df_indexHigh correlation
pr is highly correlated with umidadeHigh correlation
umidade is highly correlated with prHigh correlation
ats is highly correlated with atl3 and 1 other fieldsHigh correlation
atl3 is highly correlated with ats and 1 other fieldsHigh correlation
seta is highly correlated with ats and 1 other fieldsHigh correlation
df_index is highly correlated with EMI and 7 other fieldsHigh correlation
pr is highly correlated with umidadeHigh correlation
umidade is highly correlated with pr and 1 other fieldsHigh correlation
vento_vertical is highly correlated with regiao_hidro and 1 other fieldsHigh correlation
vorticidade is highly correlated with latHigh correlation
EMI is highly correlated with df_index and 7 other fieldsHigh correlation
nino3 is highly correlated with df_index and 7 other fieldsHigh correlation
atn is highly correlated with df_index and 8 other fieldsHigh correlation
ats is highly correlated with df_index and 6 other fieldsHigh correlation
atlgrad is highly correlated with df_index and 7 other fieldsHigh correlation
atl3 is highly correlated with df_index and 7 other fieldsHigh correlation
seta is highly correlated with df_index and 7 other fieldsHigh correlation
nesta is highly correlated with df_index and 6 other fieldsHigh correlation
regiao_hidro is highly correlated with vento_vertical and 2 other fieldsHigh correlation
lat is highly correlated with vorticidade and 2 other fieldsHigh correlation
lon is highly correlated with vento_vertical and 2 other fieldsHigh correlation
df_index is uniformly distributed Uniform
data is uniformly distributed Uniform
posicao is uniformly distributed Uniform
df_index has unique values Unique
vento_vertical has 717 (2.5%) zeros Zeros
seta has 390 (1.4%) zeros Zeros
nesta has 585 (2.1%) zeros Zeros

Reproduction

Analysis started2022-06-04 15:43:40.588035
Analysis finished2022-06-04 15:44:05.482576
Duration24.89 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct28470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82094.5
Minimum67860
Maximum96329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size222.5 KiB
2022-06-04T12:44:05.542184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum67860
5-th percentile69283.45
Q174977.25
median82094.5
Q389211.75
95-th percentile94905.55
Maximum96329
Range28469
Interquartile range (IQR)14234.5

Descriptive statistics

Standard deviation8218.725418
Coefficient of variation (CV)0.1001129846
Kurtosis-1.2
Mean82094.5
Median Absolute Deviation (MAD)7117.5
Skewness0
Sum2337230415
Variance67547447.5
MonotonicityStrictly increasing
2022-06-04T12:44:05.621816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
678601
 
< 0.1%
868361
 
< 0.1%
868471
 
< 0.1%
868461
 
< 0.1%
868451
 
< 0.1%
868441
 
< 0.1%
868431
 
< 0.1%
868421
 
< 0.1%
868411
 
< 0.1%
868401
 
< 0.1%
Other values (28460)28460
> 99.9%
ValueCountFrequency (%)
678601
< 0.1%
678611
< 0.1%
678621
< 0.1%
678631
< 0.1%
678641
< 0.1%
678651
< 0.1%
678661
< 0.1%
678671
< 0.1%
678681
< 0.1%
678691
< 0.1%
ValueCountFrequency (%)
963291
< 0.1%
963281
< 0.1%
963271
< 0.1%
963261
< 0.1%
963251
< 0.1%
963241
< 0.1%
963231
< 0.1%
963221
< 0.1%
963211
< 0.1%
963201
< 0.1%

data
Categorical

HIGH CARDINALITY
UNIFORM

Distinct146
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size222.5 KiB
2010-01-01
 
195
2019-02-01
 
195
2017-10-01
 
195
2017-11-01
 
195
2017-12-01
 
195
Other values (141)
27495 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters284700
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010-01-01
2nd row2010-01-01
3rd row2010-01-01
4th row2010-01-01
5th row2010-01-01

Common Values

ValueCountFrequency (%)
2010-01-01195
 
0.7%
2019-02-01195
 
0.7%
2017-10-01195
 
0.7%
2017-11-01195
 
0.7%
2017-12-01195
 
0.7%
2018-01-01195
 
0.7%
2018-02-01195
 
0.7%
2018-03-01195
 
0.7%
2018-04-01195
 
0.7%
2018-05-01195
 
0.7%
Other values (136)26520
93.2%

Length

2022-06-04T12:44:05.693274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2010-01-01195
 
0.7%
2012-12-01195
 
0.7%
2011-06-01195
 
0.7%
2010-09-01195
 
0.7%
2010-03-01195
 
0.7%
2010-04-01195
 
0.7%
2010-05-01195
 
0.7%
2010-06-01195
 
0.7%
2010-07-01195
 
0.7%
2010-08-01195
 
0.7%
Other values (136)26520
93.2%

Most occurring characters

ValueCountFrequency (%)
085410
30.0%
168445
24.0%
-56940
20.0%
241145
14.5%
94680
 
1.6%
74680
 
1.6%
84680
 
1.6%
34680
 
1.6%
44680
 
1.6%
54680
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number227760
80.0%
Dash Punctuation56940
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
085410
37.5%
168445
30.1%
241145
18.1%
94680
 
2.1%
74680
 
2.1%
84680
 
2.1%
34680
 
2.1%
44680
 
2.1%
54680
 
2.1%
64680
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
-56940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common284700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
085410
30.0%
168445
24.0%
-56940
20.0%
241145
14.5%
94680
 
1.6%
74680
 
1.6%
84680
 
1.6%
34680
 
1.6%
44680
 
1.6%
54680
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII284700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
085410
30.0%
168445
24.0%
-56940
20.0%
241145
14.5%
94680
 
1.6%
74680
 
1.6%
84680
 
1.6%
34680
 
1.6%
44680
 
1.6%
54680
 
1.6%

posicao
Categorical

HIGH CARDINALITY
UNIFORM

Distinct195
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size222.5 KiB
(-4.75, -39.25)
 
146
(-3.25, -39.75)
 
146
(-3.0, -40.5)
 
146
(-3.0, -40.25)
 
146
(-4.5, -39.75)
 
146
Other values (190)
27740 

Length

Max length15
Median length14
Mean length14.01025641
Min length13

Characters and Unicode

Total characters398872
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(-4.75, -39.25)
2nd row(-4.75, -39.0)
3rd row(-4.75, -38.75)
4th row(-4.75, -38.5)
5th row(-4.5, -39.0)

Common Values

ValueCountFrequency (%)
(-4.75, -39.25)146
 
0.5%
(-3.25, -39.75)146
 
0.5%
(-3.0, -40.5)146
 
0.5%
(-3.0, -40.25)146
 
0.5%
(-4.5, -39.75)146
 
0.5%
(-4.5, -39.25)146
 
0.5%
(-4.25, -39.75)146
 
0.5%
(-4.25, -39.5)146
 
0.5%
(-4.25, -39.25)146
 
0.5%
(-4.25, -39.0)146
 
0.5%
Other values (185)27010
94.9%

Length

2022-06-04T12:44:05.756748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
40.02628
 
4.6%
39.752628
 
4.6%
40.252628
 
4.6%
39.02628
 
4.6%
39.252628
 
4.6%
39.52482
 
4.4%
40.52336
 
4.1%
38.752336
 
4.1%
4.752190
 
3.8%
40.752190
 
3.8%
Other values (26)32266
56.7%

Most occurring characters

ValueCountFrequency (%)
-56940
14.3%
.56940
14.3%
549348
12.4%
(28470
7.1%
,28470
7.1%
28470
7.1%
)28470
7.1%
024528
6.1%
322338
 
5.6%
419418
 
4.9%
Other values (6)55480
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number171112
42.9%
Other Punctuation85410
21.4%
Dash Punctuation56940
 
14.3%
Open Punctuation28470
 
7.1%
Space Separator28470
 
7.1%
Close Punctuation28470
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
549348
28.8%
024528
14.3%
322338
13.1%
419418
 
11.3%
717666
 
10.3%
214454
 
8.4%
910366
 
6.1%
85840
 
3.4%
65256
 
3.1%
11898
 
1.1%
Other Punctuation
ValueCountFrequency (%)
.56940
66.7%
,28470
33.3%
Dash Punctuation
ValueCountFrequency (%)
-56940
100.0%
Open Punctuation
ValueCountFrequency (%)
(28470
100.0%
Space Separator
ValueCountFrequency (%)
28470
100.0%
Close Punctuation
ValueCountFrequency (%)
)28470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common398872
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
-56940
14.3%
.56940
14.3%
549348
12.4%
(28470
7.1%
,28470
7.1%
28470
7.1%
)28470
7.1%
024528
6.1%
322338
 
5.6%
419418
 
4.9%
Other values (6)55480
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII398872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-56940
14.3%
.56940
14.3%
549348
12.4%
(28470
7.1%
,28470
7.1%
28470
7.1%
)28470
7.1%
024528
6.1%
322338
 
5.6%
419418
 
4.9%
Other values (6)55480
13.9%

pr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12744
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.4049863
Minimum0.3
Maximum665.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size222.5 KiB
2022-06-04T12:44:05.833132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile1.91
Q15.26
median26.82
Q398.3
95-th percentile230.5155
Maximum665.07
Range664.77
Interquartile range (IQR)93.04

Descriptive statistics

Standard deviation80.72883873
Coefficient of variation (CV)1.273225395
Kurtosis3.916330489
Mean63.4049863
Median Absolute Deviation (MAD)23.95
Skewness1.857184667
Sum1805139.96
Variance6517.145403
MonotonicityNot monotonic
2022-06-04T12:44:05.908852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.9133
 
0.1%
2.533
 
0.1%
2.5932
 
0.1%
4.1632
 
0.1%
3.3831
 
0.1%
2.7230
 
0.1%
3.5230
 
0.1%
3.7130
 
0.1%
3.7530
 
0.1%
3.5729
 
0.1%
Other values (12734)28160
98.9%
ValueCountFrequency (%)
0.31
 
< 0.1%
0.323
< 0.1%
0.361
 
< 0.1%
0.372
< 0.1%
0.381
 
< 0.1%
0.392
< 0.1%
0.42
< 0.1%
0.413
< 0.1%
0.423
< 0.1%
0.433
< 0.1%
ValueCountFrequency (%)
665.071
< 0.1%
613.121
< 0.1%
574.391
< 0.1%
563.781
< 0.1%
553.831
< 0.1%
553.141
< 0.1%
546.731
< 0.1%
544.881
< 0.1%
534.771
< 0.1%
525.961
< 0.1%

divergencia
Real number (ℝ)

Distinct15565
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.815667961 × 10-7
Minimum-1.13971 × 10-5
Maximum1.85255 × 10-5
Zeros0
Zeros (%)0.0%
Negative17043
Negative (%)59.9%
Memory size222.5 KiB
2022-06-04T12:44:05.989825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.13971 × 10-5
5-th percentile-5.293701 × 10-6
Q1-2.79908 × 10-6
median-9.01039 × 10-7
Q31.8966 × 10-6
95-th percentile7.151161 × 10-6
Maximum1.85255 × 10-5
Range2.99226 × 10-5
Interquartile range (IQR)4.69568 × 10-6

Descriptive statistics

Standard deviation3.82726483 × 10-6
Coefficient of variation (CV)-21.07910098
Kurtosis1.048574057
Mean-1.815667961 × 10-7
Median Absolute Deviation (MAD)2.22365 × 10-6
Skewness0.8737554957
Sum-0.005169206686
Variance1.464795608 × 10-11
MonotonicityNot monotonic
2022-06-04T12:44:06.067725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.81175 × 10-69
 
< 0.1%
-2.90906 × 10-69
 
< 0.1%
-1.6159 × 10-69
 
< 0.1%
-2.16578 × 10-69
 
< 0.1%
-1.05983 × 10-68
 
< 0.1%
-1.53063 × 10-68
 
< 0.1%
-3.05426 × 10-68
 
< 0.1%
-7.32983 × 10-78
 
< 0.1%
-2.34867 × 10-68
 
< 0.1%
-6.7058 × 10-78
 
< 0.1%
Other values (15555)28386
99.7%
ValueCountFrequency (%)
-1.13971 × 10-51
< 0.1%
-1.1289 × 10-51
< 0.1%
-1.12056 × 10-51
< 0.1%
-1.07008 × 10-51
< 0.1%
-1.06823 × 10-51
< 0.1%
-1.06452 × 10-51
< 0.1%
-1.05927 × 10-51
< 0.1%
-1.05142 × 10-51
< 0.1%
-1.05105 × 10-51
< 0.1%
-1.03931 × 10-51
< 0.1%
ValueCountFrequency (%)
1.85255 × 10-51
< 0.1%
1.82932 × 10-51
< 0.1%
1.82314 × 10-51
< 0.1%
1.79843 × 10-51
< 0.1%
1.76617 × 10-51
< 0.1%
1.7519 × 10-51
< 0.1%
1.73769 × 10-51
< 0.1%
1.73207 × 10-51
< 0.1%
1.72923 × 10-51
< 0.1%
1.68802 × 10-51
< 0.1%

umidade
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3447
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.15834106
Minimum48.86
Maximum90.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size222.5 KiB
2022-06-04T12:44:06.150231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum48.86
5-th percentile59.65
Q166.22
median73.82
Q380.17
95-th percentile85.37
Maximum90.12
Range41.26
Interquartile range (IQR)13.95

Descriptive statistics

Standard deviation8.322605987
Coefficient of variation (CV)0.1137615461
Kurtosis-1.014163196
Mean73.15834106
Median Absolute Deviation (MAD)6.9
Skewness-0.1925936516
Sum2082817.97
Variance69.26577041
MonotonicityNot monotonic
2022-06-04T12:44:06.228117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.0625
 
0.1%
79.3824
 
0.1%
83.0523
 
0.1%
78.323
 
0.1%
78.4722
 
0.1%
80.0621
 
0.1%
78.6821
 
0.1%
78.9521
 
0.1%
68.6920
 
0.1%
71.6320
 
0.1%
Other values (3437)28250
99.2%
ValueCountFrequency (%)
48.861
< 0.1%
49.161
< 0.1%
49.321
< 0.1%
49.391
< 0.1%
49.511
< 0.1%
50.331
< 0.1%
50.341
< 0.1%
50.361
< 0.1%
50.491
< 0.1%
50.61
< 0.1%
ValueCountFrequency (%)
90.122
< 0.1%
89.791
< 0.1%
89.651
< 0.1%
89.531
< 0.1%
89.521
< 0.1%
89.451
< 0.1%
89.441
< 0.1%
89.422
< 0.1%
89.391
< 0.1%
89.281
< 0.1%

vento_vertical
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct126
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.04648647699
Minimum-0.55
Maximum0.79
Zeros717
Zeros (%)2.5%
Negative20484
Negative (%)71.9%
Memory size222.5 KiB
2022-06-04T12:44:06.306732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.55
5-th percentile-0.23
Q1-0.12
median-0.06
Q30.01
95-th percentile0.19
Maximum0.79
Range1.34
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.1304509834
Coefficient of variation (CV)-2.806213588
Kurtosis3.248196767
Mean-0.04648647699
Median Absolute Deviation (MAD)0.06
Skewness1.06616869
Sum-1323.47
Variance0.01701745907
MonotonicityNot monotonic
2022-06-04T12:44:06.392861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.061426
 
5.0%
-0.071424
 
5.0%
-0.081326
 
4.7%
-0.051324
 
4.7%
-0.091230
 
4.3%
-0.11200
 
4.2%
-0.041173
 
4.1%
-0.031088
 
3.8%
-0.111035
 
3.6%
-0.12956
 
3.4%
Other values (116)16288
57.2%
ValueCountFrequency (%)
-0.551
 
< 0.1%
-0.521
 
< 0.1%
-0.511
 
< 0.1%
-0.492
 
< 0.1%
-0.474
< 0.1%
-0.462
 
< 0.1%
-0.451
 
< 0.1%
-0.442
 
< 0.1%
-0.437
< 0.1%
-0.428
< 0.1%
ValueCountFrequency (%)
0.791
 
< 0.1%
0.761
 
< 0.1%
0.753
< 0.1%
0.741
 
< 0.1%
0.723
< 0.1%
0.711
 
< 0.1%
0.72
< 0.1%
0.692
< 0.1%
0.673
< 0.1%
0.662
< 0.1%

vorticidade
Real number (ℝ)

HIGH CORRELATION

Distinct12274
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.850243754 × 10-6
Minimum-3.0734 × 10-5
Maximum5.02077 × 10-5
Zeros0
Zeros (%)0.0%
Negative3826
Negative (%)13.4%
Memory size222.5 KiB
2022-06-04T12:44:06.483108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-3.0734 × 10-5
5-th percentile-4.6045695 × 10-6
Q13.28607 × 10-6
median8.79077 × 10-6
Q31.43367 × 10-5
95-th percentile2.27644 × 10-5
Maximum5.02077 × 10-5
Range8.09417 × 10-5
Interquartile range (IQR)1.105063 × 10-5

Descriptive statistics

Standard deviation8.597553265 × 10-6
Coefficient of variation (CV)0.9714481887
Kurtosis0.7698947903
Mean8.850243754 × 10-6
Median Absolute Deviation (MAD)5.52531 × 10-6
Skewness-0.07528731449
Sum0.2519664397
Variance7.391792214 × 10-11
MonotonicityNot monotonic
2022-06-04T12:44:06.581592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.05707 × 10-513
 
< 0.1%
1.53103 × 10-511
 
< 0.1%
1.0305 × 10-511
 
< 0.1%
9.61545 × 10-611
 
< 0.1%
1.27882 × 10-511
 
< 0.1%
8.14249 × 10-610
 
< 0.1%
6.72909 × 10-69
 
< 0.1%
7.42777 × 10-69
 
< 0.1%
6.37173 × 10-69
 
< 0.1%
7.15975 × 10-69
 
< 0.1%
Other values (12264)28367
99.6%
ValueCountFrequency (%)
-3.0734 × 10-51
< 0.1%
-2.88991 × 10-51
< 0.1%
-2.78545 × 10-51
< 0.1%
-2.76209 × 10-51
< 0.1%
-2.74903 × 10-51
< 0.1%
-2.74124 × 10-51
< 0.1%
-2.73643 × 10-51
< 0.1%
-2.72452 × 10-51
< 0.1%
-2.63174 × 10-51
< 0.1%
-2.61594 × 10-51
< 0.1%
ValueCountFrequency (%)
5.02077 × 10-51
< 0.1%
4.58347 × 10-51
< 0.1%
4.56262 × 10-51
< 0.1%
4.51887 × 10-51
< 0.1%
4.50375 × 10-51
< 0.1%
4.36745 × 10-51
< 0.1%
4.32965 × 10-51
< 0.1%
4.29941 × 10-51
< 0.1%
4.29552 × 10-51
< 0.1%
4.28842 × 10-51
< 0.1%

fluxo_energia
Real number (ℝ)

Distinct13045
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.68395609
Minimum-1181.83
Maximum1081.59
Zeros0
Zeros (%)0.0%
Negative9733
Negative (%)34.2%
Memory size222.5 KiB
2022-06-04T12:44:06.669641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1181.83
5-th percentile-231.0505
Q1-40.12
median55.93
Q3147.56
95-th percentile309.422
Maximum1081.59
Range2263.42
Interquartile range (IQR)187.68

Descriptive statistics

Standard deviation174.3014694
Coefficient of variation (CV)3.50820432
Kurtosis3.260775841
Mean49.68395609
Median Absolute Deviation (MAD)93.66
Skewness-0.3120302366
Sum1414502.23
Variance30381.00224
MonotonicityNot monotonic
2022-06-04T12:44:06.748701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.5715
 
0.1%
81.5414
 
< 0.1%
-46.5912
 
< 0.1%
186.4111
 
< 0.1%
41.3211
 
< 0.1%
31.3511
 
< 0.1%
71.5811
 
< 0.1%
17.5511
 
< 0.1%
28.911
 
< 0.1%
69.4111
 
< 0.1%
Other values (13035)28352
99.6%
ValueCountFrequency (%)
-1181.831
< 0.1%
-1111.381
< 0.1%
-1066.151
< 0.1%
-1055.361
< 0.1%
-1010.21
< 0.1%
-964.371
< 0.1%
-935.491
< 0.1%
-935.461
< 0.1%
-928.41
< 0.1%
-925.521
< 0.1%
ValueCountFrequency (%)
1081.591
< 0.1%
1040.541
< 0.1%
1036.041
< 0.1%
1000.781
< 0.1%
976.161
< 0.1%
971.231
< 0.1%
941.851
< 0.1%
932.881
< 0.1%
931.491
< 0.1%
930.641
< 0.1%

EMI
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct112
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03116438356
Minimum-1.7
Maximum1.22
Zeros195
Zeros (%)0.7%
Negative13260
Negative (%)46.6%
Memory size222.5 KiB
2022-06-04T12:44:06.828399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.7
5-th percentile-1.28
Q1-0.45
median0.03
Q30.46
95-th percentile0.98
Maximum1.22
Range2.92
Interquartile range (IQR)0.91

Descriptive statistics

Standard deviation0.6518870465
Coefficient of variation (CV)-20.91769424
Kurtosis-0.2929169189
Mean-0.03116438356
Median Absolute Deviation (MAD)0.45
Skewness-0.4185585026
Sum-887.25
Variance0.4249567214
MonotonicityNot monotonic
2022-06-04T12:44:06.905301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.77585
 
2.1%
0.35585
 
2.1%
-0.08585
 
2.1%
0.03585
 
2.1%
-0.88390
 
1.4%
0.33390
 
1.4%
0.45390
 
1.4%
-0.09390
 
1.4%
-1.11390
 
1.4%
0.02390
 
1.4%
Other values (102)23790
83.6%
ValueCountFrequency (%)
-1.7195
0.7%
-1.63195
0.7%
-1.51195
0.7%
-1.47195
0.7%
-1.46195
0.7%
-1.42195
0.7%
-1.41195
0.7%
-1.28195
0.7%
-1.18195
0.7%
-1.12195
0.7%
ValueCountFrequency (%)
1.22195
0.7%
1.14195
0.7%
1.12195
0.7%
1.09195
0.7%
1.08195
0.7%
1.02195
0.7%
1195
0.7%
0.98195
0.7%
0.97195
0.7%
0.93195
0.7%

nino3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct112
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.08226027397
Minimum-1.52
Maximum2.81
Zeros195
Zeros (%)0.7%
Negative16575
Negative (%)58.2%
Memory size222.5 KiB
2022-06-04T12:44:06.989709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.52
5-th percentile-1.24
Q1-0.68
median-0.2
Q30.35
95-th percentile1.81
Maximum2.81
Range4.33
Interquartile range (IQR)1.03

Descriptive statistics

Standard deviation0.8654106429
Coefficient of variation (CV)-10.52039583
Kurtosis1.858766754
Mean-0.08226027397
Median Absolute Deviation (MAD)0.51
Skewness1.149046219
Sum-2341.95
Variance0.7489355809
MonotonicityNot monotonic
2022-06-04T12:44:07.066584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.35780
 
2.7%
-0.71585
 
2.1%
-0.44585
 
2.1%
0.08390
 
1.4%
0.57390
 
1.4%
-0.94390
 
1.4%
-0.57390
 
1.4%
-0.64390
 
1.4%
-0.34390
 
1.4%
0.09390
 
1.4%
Other values (102)23790
83.6%
ValueCountFrequency (%)
-1.52195
0.7%
-1.51195
0.7%
-1.5195
0.7%
-1.33195
0.7%
-1.31195
0.7%
-1.28195
0.7%
-1.27195
0.7%
-1.24195
0.7%
-1.22195
0.7%
-1.17390
1.4%
ValueCountFrequency (%)
2.81390
1.4%
2.59195
0.7%
2.58195
0.7%
2.47195
0.7%
2.05195
0.7%
1.98195
0.7%
1.81195
0.7%
1.55195
0.7%
1.41195
0.7%
1.06195
0.7%

atn
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct82
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1664383562
Minimum-0.6
Maximum1.29
Zeros195
Zeros (%)0.7%
Negative8970
Negative (%)31.5%
Memory size222.5 KiB
2022-06-04T12:44:07.150271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.6
5-th percentile-0.32
Q1-0.06
median0.185
Q30.34
95-th percentile0.73
Maximum1.29
Range1.89
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.3317916545
Coefficient of variation (CV)1.993480722
Kurtosis0.9305071546
Mean0.1664383562
Median Absolute Deviation (MAD)0.195
Skewness0.5656781169
Sum4738.5
Variance0.110085702
MonotonicityNot monotonic
2022-06-04T12:44:07.232304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.341170
 
4.1%
0.3975
 
3.4%
0.22780
 
2.7%
0.32780
 
2.7%
0.09780
 
2.7%
0.12585
 
2.1%
-0.23585
 
2.1%
-0.06585
 
2.1%
0.63585
 
2.1%
0.23585
 
2.1%
Other values (72)21060
74.0%
ValueCountFrequency (%)
-0.6195
0.7%
-0.47195
0.7%
-0.46195
0.7%
-0.44195
0.7%
-0.41195
0.7%
-0.4195
0.7%
-0.37195
0.7%
-0.32195
0.7%
-0.31195
0.7%
-0.29195
0.7%
ValueCountFrequency (%)
1.29195
 
0.7%
1.22195
 
0.7%
1.08195
 
0.7%
1.01195
 
0.7%
0.97195
 
0.7%
0.81195
 
0.7%
0.73390
1.4%
0.71195
 
0.7%
0.66195
 
0.7%
0.63585
2.1%

ats
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct84
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1674657534
Minimum-0.76
Maximum1.03
Zeros195
Zeros (%)0.7%
Negative7995
Negative (%)28.1%
Memory size222.5 KiB
2022-06-04T12:44:07.320641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.76
5-th percentile-0.38
Q1-0.04
median0.19
Q30.37
95-th percentile0.69
Maximum1.03
Range1.79
Interquartile range (IQR)0.41

Descriptive statistics

Standard deviation0.3224212279
Coefficient of variation (CV)1.925296494
Kurtosis0.0996244813
Mean0.1674657534
Median Absolute Deviation (MAD)0.21
Skewness-0.1578649548
Sum4767.75
Variance0.1039554482
MonotonicityNot monotonic
2022-06-04T12:44:07.418061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.23975
 
3.4%
0.28780
 
2.7%
0.08780
 
2.7%
0.15780
 
2.7%
-0.1585
 
2.1%
0.19585
 
2.1%
0.8585
 
2.1%
0.17585
 
2.1%
0.21585
 
2.1%
0.37585
 
2.1%
Other values (74)21645
76.0%
ValueCountFrequency (%)
-0.76195
0.7%
-0.69195
0.7%
-0.53195
0.7%
-0.52195
0.7%
-0.43195
0.7%
-0.42195
0.7%
-0.41195
0.7%
-0.38390
1.4%
-0.37195
0.7%
-0.3390
1.4%
ValueCountFrequency (%)
1.03195
 
0.7%
0.84195
 
0.7%
0.8585
2.1%
0.71390
1.4%
0.69195
 
0.7%
0.68390
1.4%
0.67195
 
0.7%
0.66195
 
0.7%
0.62195
 
0.7%
0.6390
1.4%

atlgrad
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct90
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.001712328767
Minimum-0.93
Maximum0.69
Zeros195
Zeros (%)0.7%
Negative12675
Negative (%)44.5%
Memory size222.5 KiB
2022-06-04T12:44:07.501770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.93
5-th percentile-0.63
Q1-0.2
median0.05
Q30.27
95-th percentile0.47
Maximum0.69
Range1.62
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation0.3548636406
Coefficient of variation (CV)-207.2403661
Kurtosis-0.2396207164
Mean-0.001712328767
Median Absolute Deviation (MAD)0.245
Skewness-0.5519599546
Sum-48.75
Variance0.1259282034
MonotonicityNot monotonic
2022-06-04T12:44:07.582757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.311170
 
4.1%
-0.06780
 
2.7%
0.36780
 
2.7%
-0.05780
 
2.7%
0.16780
 
2.7%
-0.15585
 
2.1%
-0.27585
 
2.1%
0.1585
 
2.1%
0.09585
 
2.1%
-0.04585
 
2.1%
Other values (80)21255
74.7%
ValueCountFrequency (%)
-0.93195
0.7%
-0.92195
0.7%
-0.9195
0.7%
-0.87195
0.7%
-0.67195
0.7%
-0.65195
0.7%
-0.63390
1.4%
-0.62195
0.7%
-0.6390
1.4%
-0.59195
0.7%
ValueCountFrequency (%)
0.69390
1.4%
0.62195
0.7%
0.57195
0.7%
0.53195
0.7%
0.51195
0.7%
0.5195
0.7%
0.47390
1.4%
0.46195
0.7%
0.45390
1.4%
0.43195
0.7%

atl3
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct95
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1733561644
Minimum-0.77
Maximum1.36
Zeros0
Zeros (%)0.0%
Negative9555
Negative (%)33.6%
Memory size222.5 KiB
2022-06-04T12:44:07.669524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.77
5-th percentile-0.56
Q1-0.09
median0.18
Q30.44
95-th percentile0.94
Maximum1.36
Range2.13
Interquartile range (IQR)0.53

Descriptive statistics

Standard deviation0.4352611169
Coefficient of variation (CV)2.510791113
Kurtosis-0.02119331421
Mean0.1733561644
Median Absolute Deviation (MAD)0.27
Skewness0.2117857256
Sum4935.45
Variance0.1894522399
MonotonicityNot monotonic
2022-06-04T12:44:07.746860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.091170
 
4.1%
0.1780
 
2.7%
0.65780
 
2.7%
0.33780
 
2.7%
0.23585
 
2.1%
0.5585
 
2.1%
0.14585
 
2.1%
-0.43585
 
2.1%
0.03585
 
2.1%
0.53585
 
2.1%
Other values (85)21450
75.3%
ValueCountFrequency (%)
-0.77195
0.7%
-0.71390
1.4%
-0.69195
0.7%
-0.64195
0.7%
-0.62195
0.7%
-0.57195
0.7%
-0.56195
0.7%
-0.53195
0.7%
-0.48195
0.7%
-0.44195
0.7%
ValueCountFrequency (%)
1.36195
0.7%
1.27195
0.7%
1.22195
0.7%
1.21195
0.7%
1.12195
0.7%
1.06195
0.7%
1.01195
0.7%
0.94195
0.7%
0.92195
0.7%
0.81195
0.7%

seta
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct90
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1847260274
Minimum-0.78
Maximum1.17
Zeros390
Zeros (%)1.4%
Negative8580
Negative (%)30.1%
Memory size222.5 KiB
2022-06-04T12:44:07.826860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.78
5-th percentile-0.34
Q1-0.06
median0.195
Q30.43
95-th percentile0.77
Maximum1.17
Range1.95
Interquartile range (IQR)0.49

Descriptive statistics

Standard deviation0.3529084414
Coefficient of variation (CV)1.910442434
Kurtosis0.3550978683
Mean0.1847260274
Median Absolute Deviation (MAD)0.235
Skewness0.05808993466
Sum5259.15
Variance0.124544368
MonotonicityNot monotonic
2022-06-04T12:44:07.905785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.18975
 
3.4%
0.41780
 
2.7%
0.07780
 
2.7%
0.43780
 
2.7%
0.45585
 
2.1%
-0.08585
 
2.1%
0.36585
 
2.1%
0.24585
 
2.1%
0.44585
 
2.1%
-0.17585
 
2.1%
Other values (80)21645
76.0%
ValueCountFrequency (%)
-0.78195
0.7%
-0.75195
0.7%
-0.65195
0.7%
-0.49195
0.7%
-0.45195
0.7%
-0.44195
0.7%
-0.37195
0.7%
-0.34195
0.7%
-0.32195
0.7%
-0.3195
0.7%
ValueCountFrequency (%)
1.17195
0.7%
1.13195
0.7%
1.05195
0.7%
1195
0.7%
0.98195
0.7%
0.86195
0.7%
0.81195
0.7%
0.77195
0.7%
0.69195
0.7%
0.67195
0.7%

nesta
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct77
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2569178082
Minimum-0.57
Maximum0.81
Zeros585
Zeros (%)2.1%
Negative4485
Negative (%)15.8%
Memory size222.5 KiB
2022-06-04T12:44:07.988569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.57
5-th percentile-0.2
Q10.06
median0.3
Q30.45
95-th percentile0.61
Maximum0.81
Range1.38
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation0.2542009289
Coefficient of variation (CV)0.9894251031
Kurtosis0.1467266864
Mean0.2569178082
Median Absolute Deviation (MAD)0.19
Skewness-0.5665265934
Sum7314.45
Variance0.06461811225
MonotonicityNot monotonic
2022-06-04T12:44:08.072696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.51975
 
3.4%
0.3975
 
3.4%
0.49975
 
3.4%
0.03780
 
2.7%
0.4780
 
2.7%
0.45780
 
2.7%
0.52780
 
2.7%
0.63585
 
2.1%
0.48585
 
2.1%
0.43585
 
2.1%
Other values (67)20670
72.6%
ValueCountFrequency (%)
-0.57195
0.7%
-0.54195
0.7%
-0.26390
1.4%
-0.25390
1.4%
-0.22195
0.7%
-0.2195
0.7%
-0.17195
0.7%
-0.16195
0.7%
-0.12195
0.7%
-0.1195
0.7%
ValueCountFrequency (%)
0.81195
 
0.7%
0.73195
 
0.7%
0.71195
 
0.7%
0.64195
 
0.7%
0.63585
2.1%
0.61195
 
0.7%
0.59390
1.4%
0.58195
 
0.7%
0.57195
 
0.7%
0.56390
1.4%

regiao_hidro
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size222.5 KiB
Bacia do Alto Jaguaribe
4818 
Bacia do Banabuiú
3942 
Bacia Metropolitana
2774 
Bacia do Acaraú
2628 
Bacia do Salgado
2336 
Other values (7)
11972 

Length

Max length28
Median length23
Mean length19.4
Min length13

Characters and Unicode

Total characters552318
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBacia Metropolitana
2nd rowBacia Metropolitana
3rd rowBacia Metropolitana
4th rowBacia Metropolitana
5th rowBacia Metropolitana

Common Values

ValueCountFrequency (%)
Bacia do Alto Jaguaribe4818
16.9%
Bacia do Banabuiú3942
13.8%
Bacia Metropolitana2774
9.7%
Bacia do Acaraú2628
9.2%
Bacia do Salgado2336
8.2%
Bacia do Medio Jaguaribe2190
7.7%
Bacia do Coreaú1898
 
6.7%
Bacia do Curu1898
 
6.7%
Bacia dos Sertões de Crateús1898
 
6.7%
Bacia do Litoral1752
 
6.2%
Other values (2)2336
8.2%

Length

2022-06-04T12:44:08.170273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bacia28470
29.4%
do22776
23.5%
jaguaribe8322
 
8.6%
alto4818
 
5.0%
banabuiú3942
 
4.1%
metropolitana2774
 
2.9%
acaraú2628
 
2.7%
salgado2336
 
2.4%
medio2190
 
2.3%
da2044
 
2.1%
Other values (10)16498
17.0%

Most occurring characters

ValueCountFrequency (%)
a109938
19.9%
68328
12.4%
i49786
9.0%
o44530
 
8.1%
B33726
 
6.1%
d33142
 
6.0%
c31098
 
5.6%
r25112
 
4.5%
e23798
 
4.3%
u16060
 
2.9%
Other values (17)116800
21.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter415808
75.3%
Space Separator68328
 
12.4%
Uppercase Letter68182
 
12.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a109938
26.4%
i49786
12.0%
o44530
10.7%
d33142
 
8.0%
c31098
 
7.5%
r25112
 
6.0%
e23798
 
5.7%
u16060
 
3.9%
t15914
 
3.8%
b14308
 
3.4%
Other values (8)52122
12.5%
Uppercase Letter
ValueCountFrequency (%)
B33726
49.5%
J8322
 
12.2%
A7446
 
10.9%
C5694
 
8.4%
S5256
 
7.7%
M4964
 
7.3%
L1752
 
2.6%
I1022
 
1.5%
Space Separator
ValueCountFrequency (%)
68328
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin483990
87.6%
Common68328
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a109938
22.7%
i49786
10.3%
o44530
9.2%
B33726
 
7.0%
d33142
 
6.8%
c31098
 
6.4%
r25112
 
5.2%
e23798
 
4.9%
u16060
 
3.3%
t15914
 
3.3%
Other values (16)100886
20.8%
Common
ValueCountFrequency (%)
68328
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII540054
97.8%
None12264
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a109938
20.4%
68328
12.7%
i49786
9.2%
o44530
8.2%
B33726
 
6.2%
d33142
 
6.1%
c31098
 
5.8%
r25112
 
4.6%
e23798
 
4.4%
u16060
 
3.0%
Other values (15)104536
19.4%
None
ValueCountFrequency (%)
ú10366
84.5%
õ1898
 
15.5%

lat
Real number (ℝ)

HIGH CORRELATION

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.082051282
Minimum-7.75
Maximum-3
Zeros0
Zeros (%)0.0%
Negative28470
Negative (%)100.0%
Memory size222.5 KiB
2022-06-04T12:44:08.230902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-7.75
5-th percentile-7.25
Q1-6
median-5
Q3-4
95-th percentile-3.25
Maximum-3
Range4.75
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.219914609
Coefficient of variation (CV)-0.2400437425
Kurtosis-0.9165790853
Mean-5.082051282
Median Absolute Deviation (MAD)1
Skewness-0.1852839987
Sum-144686
Variance1.488191654
MonotonicityNot monotonic
2022-06-04T12:44:08.290830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
-4.752190
 
7.7%
-52044
 
7.2%
-4.52044
 
7.2%
-5.51752
 
6.2%
-4.251752
 
6.2%
-41752
 
6.2%
-3.751752
 
6.2%
-5.251752
 
6.2%
-61606
 
5.6%
-5.751606
 
5.6%
Other values (10)10220
35.9%
ValueCountFrequency (%)
-7.75146
 
0.5%
-7.5438
 
1.5%
-7.251168
4.1%
-71022
3.6%
-6.751022
3.6%
-6.51314
4.6%
-6.251314
4.6%
-61606
5.6%
-5.751606
5.6%
-5.51752
6.2%
ValueCountFrequency (%)
-31022
3.6%
-3.251314
4.6%
-3.51460
5.1%
-3.751752
6.2%
-41752
6.2%
-4.251752
6.2%
-4.52044
7.2%
-4.752190
7.7%
-52044
7.2%
-5.251752
6.2%

lon
Real number (ℝ)

HIGH CORRELATION

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-39.60641026
Minimum-41.25
Maximum-37.5
Zeros0
Zeros (%)0.0%
Negative28470
Negative (%)100.0%
Memory size222.5 KiB
2022-06-04T12:44:08.349099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-41.25
5-th percentile-41
Q1-40.25
median-39.75
Q3-39
95-th percentile-38.25
Maximum-37.5
Range3.75
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation0.8729839986
Coefficient of variation (CV)-0.0220414825
Kurtosis-0.8107703036
Mean-39.60641026
Median Absolute Deviation (MAD)0.75
Skewness0.1407043876
Sum-1127594.5
Variance0.7621010618
MonotonicityNot monotonic
2022-06-04T12:44:08.406899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
-39.252628
9.2%
-392628
9.2%
-40.252628
9.2%
-402628
9.2%
-39.752628
9.2%
-39.52482
8.7%
-38.752336
8.2%
-40.52336
8.2%
-40.752190
7.7%
-38.51460
 
5.1%
Other values (6)4526
15.9%
ValueCountFrequency (%)
-41.25584
 
2.1%
-411314
4.6%
-40.752190
7.7%
-40.52336
8.2%
-40.252628
9.2%
-402628
9.2%
-39.752628
9.2%
-39.52482
8.7%
-39.252628
9.2%
-392628
9.2%
ValueCountFrequency (%)
-37.5146
 
0.5%
-37.75438
 
1.5%
-38730
 
2.6%
-38.251314
4.6%
-38.51460
5.1%
-38.752336
8.2%
-392628
9.2%
-39.252628
9.2%
-39.52482
8.7%
-39.752628
9.2%

Interactions

2022-06-04T12:44:03.778677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:42.848151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:44.101661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:45.401292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:46.795398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.992602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:49.276297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:50.693829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:51.896711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:53.106915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:54.460099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:55.754817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:57.082957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:58.619216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:59.779410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:01.049385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:02.292909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:03.849001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:42.938848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:44.260898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:45.470825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:46.862665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:48.066610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:49.350554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:50.763183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:51.968933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:53.174749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:54.531096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:55.831833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:57.158987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:58.683613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:59.854064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:01.121670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:02.359221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:03.925168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:43.010364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:44.331190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:45.543363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:46.930981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:48.142076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:49.422651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:50.835403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:52.036556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:53.249094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:54.604084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:55.905858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:57.231480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:58.750907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:59.927363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:01.194889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:02.425975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:03.998829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:43.085811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:44.404244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:45.617491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.000933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:48.215181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:49.498974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:50.904394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:52.106692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:53.498925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:54.679231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:55.979942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:57.308583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:58.819826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:00.003440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:01.268012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:02.496904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:04.069199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:43.153394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:44.472124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:45.687861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.070748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:48.303616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:49.568340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:50.971141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:52.173211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:53.563140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:54.748425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:56.049948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:57.380030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:58.884180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:00.073700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:01.338871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:02.561825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:04.149195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:43.230016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:44.545999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:45.761065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.147516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:48.381819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:49.784212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:51.049711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:52.250745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:53.632831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:54.823793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:56.126174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:57.456889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:58.954555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:00.152320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:01.413170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:02.632934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:04.238099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:43.308091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:44.620500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:45.839189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.220463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:48.460222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:49.863719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:51.125311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:52.327242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:53.711249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:54.901390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:56.205861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:57.539969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:59.029010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:00.231161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:01.490512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:02.719823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:04.308024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:43.372776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:44.688414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:45.906561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.285170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:48.529301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:49.934545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:51.188251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:52.393556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:53.779868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:54.974687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:56.272727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:57.612609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:59.091838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:00.300225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:01.560430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:02.788733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:04.382146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:43.442859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:44.757743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:45.977128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.354074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:48.604909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:50.005789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:51.255680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:52.458991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:53.846115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:55.047202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:56.343465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:57.694033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:59.157108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:00.374058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:01.632972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:02.854100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:04.452761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:43.507785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:44.823313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:46.043745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.419477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:48.675344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:50.076606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:51.324051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:52.523754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:53.908033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:55.116607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:56.412716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:57.764550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:59.222940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:00.445463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:01.700498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:02.921574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:04.529802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:43.578703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:44.895401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:46.119712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.489363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:48.751317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:50.153097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:51.397884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:52.599232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:53.978636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:55.189718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:56.498189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:57.842327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:59.298876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:00.522419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:01.776270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:02.992829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:04.607862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:43.652181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:44.967940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:46.195185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.561244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:48.828604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:50.232288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:51.468058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:52.674811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:54.047231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:55.266021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:56.600019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:58.154858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:59.367867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:00.599971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:01.850083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:03.066719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:04.688432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:43.742662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:45.041863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:46.274587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.636071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:48.911340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:50.311005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:51.542080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:52.747620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:54.119618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:55.373078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:56.694481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:58.235817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:59.441087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:00.677877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:01.927264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:03.141412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:04.760146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:43.805764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:45.109309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:46.348421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.707385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:48.977383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:50.380606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:51.607548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:52.814119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:54.180931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:55.444229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:56.767783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:58.309609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:59.504461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:00.747999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:01.993742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:03.205715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:04.843623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:43.879790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:45.183506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:46.439003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.779803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:49.055272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:50.460003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:51.683932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:52.889196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:54.254678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:55.522264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:56.850778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:58.391021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:59.575207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:00.825755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:02.071412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:03.277564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:04.929557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:43.954446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:45.256151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:46.528020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.851022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:49.129436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:50.544348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:51.755598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:52.966743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:54.323354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:55.598541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:56.929573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:58.468623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:59.644869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:00.901178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:02.145606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:03.348395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:05.017225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:44.028150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:45.326328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:46.719610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:47.916793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:49.199773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:50.616383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:51.820949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:53.032306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:54.388831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:55.668524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:57.002137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:58.540812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:43:59.710054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:00.971365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:02.216529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-04T12:44:03.708794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-06-04T12:44:08.478555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-04T12:44:08.586169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-04T12:44:08.693776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-04T12:44:09.194436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-04T12:44:05.156178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-04T12:44:05.381684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexdataposicaoprdivergenciaumidadevento_verticalvorticidadefluxo_energiaEMInino3atnatsatlgradatl3setanestaregiao_hidrolatlon
0678602010-01-01(-4.75, -39.25)99.900.00000979.61-0.060.00000876.081.01.060.380.53-0.150.10.39-0.16Bacia Metropolitana-4.75-39.25
1678612010-01-01(-4.75, -39.0)75.040.00001078.74-0.070.000009106.091.01.060.380.53-0.150.10.39-0.16Bacia Metropolitana-4.75-39.00
2678622010-01-01(-4.75, -38.75)83.240.00001077.68-0.080.00001139.081.01.060.380.53-0.150.10.39-0.16Bacia Metropolitana-4.75-38.75
3678632010-01-01(-4.75, -38.5)94.630.00001176.71-0.070.000014-98.921.01.060.380.53-0.150.10.39-0.16Bacia Metropolitana-4.75-38.50
4678642010-01-01(-4.5, -39.0)89.760.00001077.70-0.000.00000671.081.01.060.380.53-0.150.10.39-0.16Bacia Metropolitana-4.50-39.00
5678652010-01-01(-4.5, -38.75)96.780.00001176.60-0.130.0000141.091.01.060.380.53-0.150.10.39-0.16Bacia Metropolitana-4.50-38.75
6678662010-01-01(-4.5, -38.5)79.990.00001075.20-0.050.000012-35.911.01.060.380.53-0.150.10.39-0.16Bacia Metropolitana-4.50-38.50
7678672010-01-01(-4.5, -38.25)57.400.00001073.53-0.060.00001863.101.01.060.380.53-0.150.10.39-0.16Bacia Metropolitana-4.50-38.25
8678682010-01-01(-4.5, -38.0)67.500.00001070.26-0.060.00002064.091.01.060.380.53-0.150.10.39-0.16Bacia Metropolitana-4.50-38.00
9678692010-01-01(-4.25, -38.75)79.770.00001175.23-0.120.00002050.081.01.060.380.53-0.150.10.39-0.16Bacia Metropolitana-4.25-38.75

Last rows

df_indexdataposicaoprdivergenciaumidadevento_verticalvorticidadefluxo_energiaEMInino3atnatsatlgradatl3setanestaregiao_hidrolatlon
28460963202022-02-01(-5.5, -40.75)16.076.646010e-0780.46-0.120.000013-227.340.04-1.170.450.40.050.120.350.51Bacia dos Sertões de Crateús-5.50-40.75
28461963212022-02-01(-5.5, -40.5)12.18-1.652970e-0680.92-0.160.000004122.660.04-1.170.450.40.050.120.350.51Bacia dos Sertões de Crateús-5.50-40.50
28462963222022-02-01(-5.5, -40.25)11.35-6.242410e-0779.45-0.070.000011122.660.04-1.170.450.40.050.120.350.51Bacia dos Sertões de Crateús-5.50-40.25
28463963232022-02-01(-5.25, -40.75)19.991.385020e-0679.53-0.140.000011-272.320.04-1.170.450.40.050.120.350.51Bacia dos Sertões de Crateús-5.25-40.75
28464963242022-02-01(-5.25, -40.5)11.83-7.027080e-0779.85-0.140.000003101.660.04-1.170.450.40.050.120.350.51Bacia dos Sertões de Crateús-5.25-40.50
28465963252022-02-01(-5.25, -40.25)12.817.554250e-0778.69-0.040.00001597.690.04-1.170.450.40.050.120.350.51Bacia dos Sertões de Crateús-5.25-40.25
28466963262022-02-01(-5.0, -40.75)27.711.383160e-0679.00-0.130.000006-135.320.04-1.170.450.40.050.120.350.51Bacia dos Sertões de Crateús-5.00-40.75
28467963272022-02-01(-5.0, -40.5)24.11-1.849470e-0779.46-0.09-0.000002120.670.04-1.170.450.40.050.120.350.51Bacia dos Sertões de Crateús-5.00-40.50
28468963282022-02-01(-5.0, -40.25)16.982.199350e-0678.71-0.050.000014292.690.04-1.170.450.40.050.120.350.51Bacia dos Sertões de Crateús-5.00-40.25
28469963292022-02-01(-4.75, -40.75)50.487.016720e-0778.93-0.110.000007232.670.04-1.170.450.40.050.120.350.51Bacia dos Sertões de Crateús-4.75-40.75